Antibiotics affect the pharmacokinetics of n-butylphthalidein vivoby altering the intestinal microbiota

Antibiotics affect the pharmacokinetics of n-butylphthalide in vivo by altering the intestinal microbiota | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Antibiotics affect the pharmacokinetics of n-butylphthalide in vivo by altering the intestinal microbiota Xiangchen Li , Xiaoli Guo , Yixin Liu , Fefei Ren , Shan Li , Xiuling Yang , Jian Liu , View ORCID Profile Zhiqing Zhang doi: https://doi.org/10.1101/2024.01.12.575425 Xiangchen Li The Second Hospital of Hebei Medical University , Shijiazhuang 050000, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Xiaoli Guo The Second Hospital of Hebei Medical University , Shijiazhuang 050000, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yixin Liu The Second Hospital of Hebei Medical University , Shijiazhuang 050000, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Fefei Ren The Second Hospital of Hebei Medical University , Shijiazhuang 050000, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Shan Li The Second Hospital of Hebei Medical University , Shijiazhuang 050000, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Xiuling Yang The Second Hospital of Hebei Medical University , Shijiazhuang 050000, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jian Liu The Second Hospital of Hebei Medical University , Shijiazhuang 050000, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: 777yyy{at}sina.cn ljhaiyang{at}aliyun.com Zhiqing Zhang The Second Hospital of Hebei Medical University , Shijiazhuang 050000, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Zhiqing Zhang For correspondence: 777yyy{at}sina.cn ljhaiyang{at}aliyun.com Abstract Full Text Info/History Metrics Preview PDF ABSTRACT Objective N-butylphthalide (NBP) is a monomeric compound extracted from natural plant celery seeds, whether intestinal microbiota alteration can modify its pharmacokinetics is still unclear. The purpose of this study is to investigate the effect of intestinal microbiota alteration on the pharmacokinetics of NBP and its related mechanisms. Methods After treatment with antibiotics and probiotics, plasma NBP concentrations in SD rats were determined by high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS). The effect of intestinal microbiota changes on NBP pharmacokinetics was compared. Intestinal microbiota changes after NBP treatment were analyzed by 16S rRNA sequencing. Expressions of CYP3A1 mRNA and protein in the liver and small intestine tissues under different intestinal flora conditions were determined by qRT-PCR and Western Blot. KEGG analysis was used to analyze the effect of intestinal microbiota changes on metabolic pathways. Results Compared to the control group, the values of C max , AUC 0-8 , AUC 0-∞ , t 1/2 in the antibiotic group increased by 56.1% ( P< 0.001), 56.4% ( P< 0.001), 53.2% ( P <0.001), and 24.4% ( P <0.05), respectively. In contrast, the CL and T max values decreased by 57.1% ( P< 0.001) and 28.6% ( P <0.05), respectively. Treatment with antibiotics could reduce the richness and diversity of the intestinal microbiota. CYP3A1 mRNA and protein expressions in the small intestine of the antibiotic group were 61.2% and 66.1% of those of the control group, respectively. CYP3A1 mRNA and protein expressions in the liver were 44.6% and 63.9% of those in the control group, respectively. There was no significant change in the probiotic group. KEGG analysis showed that multiple metabolic pathways were significantly down-regulated in the antibiotic group. Among them, the pathways of drug metabolism, bile acid biosynthesis and decomposition, and fatty acid synthesis and decomposition were related to NBP biological metabolism. Conclusion Antibiotic treatment could affect the intestinal microbiota, decrease CYP3A1 mRNA and protein expressions and increase NBP exposure in vivo by inhibiting pathways related to NBP metabolism. Introduction The intestinal microbiota is a complex system. It is an essential part of the intestinal mucosal barrier and plays a vital role in maintaining the dynamic balance of microecology in the organism [ 1 ]. Unbalanced intestinal microbiota or disruption of the dynamic balance of intestinal microbiota may bring various problems to the body at the macro level and contribute to multiple diseases [ 2 , 3 ]. On the micro level, it can also directly or indirectly change the efficacy and toxicity of drugs by affecting the expression of cytochrome P450 enzymes (CYP450) in the host body [ 4 ]. Oral antibiotics can induce intestinal microbiota depletion and down-regulate the expression of the CYP450 enzyme, while probiotics can restore enzyme activity and alter substrate metabolism [ 5 ]. The complex relations between the host, bacteria, and drugs affect the intestinal microbiota, leading to poor drug efficacy, adverse reactions, and drug-drug interactions in clinical practice [ 6 – 8 ]. N-butylphthalide (NBP), also called butylphthalide, is a monomeric compound extracted from natural plant celery seeds. It is rapidly absorbed after oral administration. NBP is mainly metabolized by CYP3A4 in the liver. Metabolites are conjugated with glucuronic acid and excreted in urine [ 9 ]. NBP can inhibit brain tissue damage and promote the absorption of inflammatory factors, and it is recommended to treat acute ischemic stroke as a neuroprotective agent [ 10 , 11 ]. Changes in autonomic nervous activity and mucin products caused by brain injury can induce specific changes in the intestinal microbiota, and ischemic stroke can induce intestinal microbiota imbalance [ 12 ]. Whether intestinal microbiota alteration can modify the pharmacokinetics of NBP is still unclear, and further in-depth studies are warranted. In this study, we used antibiotics and probiotics to change the intestinal microbiota composition. The effects of intestinal microbiota alteration on CYP3A1 expression in the small intestine and liver and the pharmacokinetics of NBP in SD rats were investigated using 16S rRNA and KEGG analyses. The results lay the foundation to clarify the effect of the intestinal microbiota on drug metabolism and related mechanisms. 1 Materials 1.1 Instruments The following instruments were used: high-performance liquid chromatography (HPLC) (Shimadzu, LC-20AD), SCIEX mass spectrometer (MS) (AB SCIEX, API 4000+), PCR amplification instrument (ABI, 2720), enzyme labeling instrument (BioTek, FLX800T, electrophoresis apparatus (Beijing Liuyi Instrument Factory, DYY-6C), a gel imaging system (Beijing Bijing Biotechnology Co., LTD., BG-gdsAUTO130), Nanodrop UV quantitative system (Thermo Fisher Scientific, NC2000), Sequencer (Illumina, Novaseq6000), and fluorescence quantitative PCR instrument (Bio-rad Corporation, CFX). 1.2 Drugs and reagents The following were used: vancomycin hydrochloride for injection (VIANEX S.A.), bifidobacterium quadruplex viable tablets (Hangzhou Yuanda Biological Pharmaceutical), NBP capsules (Shiyao Group Enbipu Pharmaceutical), NBP reference (Shiyao Group Enbipu Pharmaceutical.), glipizide reference (Sichuan Vicchi Biochemical Technology), Soil DNA Kit (Omega Bio-Tek), Quant-iT PicoGreen dsDNA Assay Kit (ABI), First Strand cDNA Synthesis Kit (Servicebio), and SYBR Green qPCR Master Mix (Servicebio). 1.3 Animals Male Sprague Dawley (SD) rats (7 weeks old, 200 ± 20 g) were purchased from Beijing Huafukang Bioscience Co. Ltd. The rats were kept in a specific pathogen-free (SPF) facility. Animal experiments were approved by the Research Ethics Committee of the Second Hospital of Hebei Medical University. 2 Methods 2.1 Quantification of NBP in plasma by HPLC-MS/MS Chromatography separation was performed on a Symmetry C 18 column (4.6 × 150 mm, 3.5 μm), and the column temperature was 40℃. The mobile phase consisted of water (A) and acetonitrile (B, containing 0.1% formic acid) with a flow rate of 0.8 mL·min -1 . The elution procedure was as follows: 0-2.0 min, 75%-95% B; 2.0-6.5 min, 95% B; 6.5– .0 min, 95%– 75% B; and 7.0-8.0 min, 75% B. The injection volume was 5 μL, and the internal standard (IS) was glipizide. 2.1.1 Mass spectrum conditions Both NBP and glipizide were monitored in positive ESI mode. The scanning mode was multireaction monitoring (MRM), with the ion transitions of m/z 191.1→45.1 and 446.2→321.0, respectively. The declustering potential (DP) and collision energy (CE) of NBP were 60V and 22V, and those of the glipizide were 100V and 23V, respectively. 2.1.2 Plasma sample processing method Acetonitrile (150 μL) containing glipizide (250 ng·mL -1 ) was added to plasma (50 μL) and centrifuged at 10,900 g for 5 min. The supernatant was transferred to the autosampler vial for HPLC-MS/MS detection. 2.1.3 Standard curves A series of NBP solutions with a concentration of 20-2000 ng·mL -1 were added to the blank plasma to prepare the simulated plasma samples, which were tested according to the plasma sample processing method. The standard curve was drawn with the concentration of NBP as the abscissa (X) and the peak area ratio of NBP to the glipizide as the ordinate (Y). The standard curve equation was obtained through regression. 2.2 Experimental design and sample collection Before the experiment, 30 SD male rats were randomly divided into three groups (n=10): the antibiotic, probiotic, and control groups. The antibiotic group received 50 mg·kg -1 of vancomycin solution. The probiotic group received 600 mg·kg -1 of live bifidobacterium tetrad bacteria suspension. The control group received physiological saline. All groups received daily gavage for 7 days. Fecal samples were collected on day 0 and day 8 before NBP administration. On day 8, all rats were gavaged using 70 mg·kg -1 of NBP solution. Blood samples (about 300 μL) were collected in heparinized centrifuge tubes at 0.08, 0.16, 0.33, 0.5, 0.75, 1, 1.5, 2, 3, 4, 6, and 8 h after gavage. Plasma samples were centrifuged at 8000 RPM for 10 min at 4℃. The rats were sacrificed after collecting blood and samples from the liver and small intestine tissues. The samples were stored at –80℃. 2.3 Effects of the intestinal microbiota on the pharmacokinetics of NBP The frozen plasma samples were thawed naturally, processed according to the “plasma sample processing method,” and injected for analysis. NBP concentrations at different time points were calculated according to the standard curve equation. The pharmacokinetic parameters were calculated using the DAS 2.0 software. Statistical analysis was performed with SPSS 20 software to investigate the effect of antibiotic or probiotic interventions on NBP pharmacokinetics. 2.4 CYP3A1 mRNA and protein expressions in the liver and small intestinal 2.4.1 Quantitative real-time PCR (qRT-PCR) to detect CYP3A1 mRNA expression The frozen small intestine and liver tissue samples were naturally thawed at room temperature. Total RNA was extracted and tested for RNA concentration and purity, followed by reverse transcription and primer amplification (primer sequence shown in Table 1 ), with three replicates per transcript. CYP3A1 was encoded by Cyp3a1 , and its internal reference was β-actin. Data processing was performed using the 2 -ΔΔCt method. View this table: View inline View popup Download powerpoint Table 1. The sequence of primers 2.4.2 Western Blot assay for CYP3A1 protein expression The frozen small intestine and liver tissue samples were thawed at room temperature. CYP3A1 total protein was extracted, added to 10% SDS-PAGE gel, and then transferred to the nitrocellulose membranes. Nitrocellulose membranes were incubated with the monoclonal CYP3A4 antibody (1: 2000 dilution) at 4℃ for 12 h, followed by the incubation with a secondary antibody (1:5000 dilution) at room temperature for 30 min. The GAPDH was used as a loading control. The Odyssey CLx Imaging System was used to detect target proteins. The protein level was normalized to the control group and expressed as multiple changes relative to the control group. 2.5 Effects of antibiotics and probiotics on the intestinal microbiota The frozen feces were naturally thawed at room temperature, and DNA was extracted. PCR amplification was performed on specific primers for the V3-V4 region of 16S rRNA bacterial genes according to the Illumina 16S metagenomic sequencing library ( Table 1 ). The amplified products were quantified and mixed for library construction and quality inspection. After the individual quantification step, amplicons were pooled in equal amounts and subjected to paired-end 2 × 250 bp sequencing to understand the effects of antibiotics and probiotics on the intestinal microbiota. 2.6 Data analysis and statistics 16S rRNA intestinal microbiota data were analyzed using the Genescloud cloud platform online. The relative abundance of the microbiota was plotted as a stacked-bar plot at the phylum and genus levels. α-diversity indices (diversity within a sample) were calculated using filtered data. Among these indices, the Chao1 diversity index estimated total richness, and the Shannon diversity index evaluated the richness and evenness of species at the genus level. β-diversity (sample dissimilarity, the difference between two samples) was calculated using the principal coordinate analysis (PCoA) based on the Bray-Curtis dissimilar matrix. Kruskal-Wallis rank sum test was used to analyze bacteria with significant abundance differences between groups, and linear discriminant analysis effect size (LEfSe) was used to analyze bacteria enrichment. Microbial functional abundances based on marker gene sequences were predicted by Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt2) in the Kyoto Encyclopedia of Genes and Genomes (KEGG). Spearman correlation analysis was performed to explore the relationship between the intestinal microbiota and the metabolic pathways of NBP. P <0.05 was considered statistically significant. 3 Results 3.1 Effects of the intestinal microbiota on the pharmacokinetics of NBP The concentration-time curve shows that the standard curve equation for NBP in the plasma of SD rats is Y=0.00359X+0.000347 ( r =0.9995) ( Figure 1 ). Table 2 shows the pharmacokinetic parameters of NBP. Compared to the control group, in the antibiotics group, the values of C max , AUC 0-8 , AUC 0-∞ , and t 1/2 increased by 56.1% ( P< 0.001), 56.4% ( P< 0.001), 53.2& ( P <0.001), and 24.4% ( P <0.05), respectively. In contrast, the CL and T max values decreased by 57.1% ( P< 0.001) and 28.6% ( P <0.05), respectively. There were no statistically significant changes in pharmacokinetic parameters between the probiotic and control groups. Download figure Open in new tab Figure 1. Blood concentration-time curve of n-butylphthalide in rats View this table: View inline View popup Download powerpoint Table 2. Pharmacokinetic parameters of n-butylphthalide in rats (mean±SD, n=10) 3.2 CYP3A1 mRNA and protein expressions in the liver and small intestine The effects of antibiotics and probiotics on CYP3A1 mRNA and protein expressions in SD rats were investigated by qRT-PCR and Western Blot ( Figure 2 ). CYP3A1 mRNA and protein expression values in the small intestine of the antibiotic group were 61.2% and 66.1% of those of the control group ( Figure 2A ), respectively. The CYP3A1 mRNA and protein expression values in the liver were 44.6% and 63.9% of those of the control group ( Figure 2B ), respectively. However, there were no significant changes in CYP3A1 mRNA and protein expressions between the probiotic and control groups. Download figure Open in new tab Figure 2. The CYP3A1mRNA and protein expressions in the small intestine and the liver (n=3) A: CYP3A1 expression in the small intestine; B: CYP3A1 expression in the liver; * P <0.05, ** P <0.01, compared to the control group 3.3 Effects of antibiotics and probiotics on the intestinal microbiota The intestinal microbiota was affected by intragastric administration of antibiotics and probiotics ( Figure 3 ). There were differences in the relative abundance of the microbiota at the phylum level between the control and the antibiotic groups ( Figure 3A ). In the antibiotic group, the relative abundance of Firmicutes and Bacteroidetes decreased with increasing Proteobacteria and Spirillum . In the probiotic group, the relative abundance of Firmicutes increased with decreased Bacteroidetes . At the intestinal microbiota genus level, the dominant bacteria in the control and probiotic groups are Lactobacillus and Prevotella ( Figure 3B ). In contrast, the prevalent bacteria in the antibiotic group are Lactobacillus and Sphaerochaeta. The relative abundance of the Lactobacillus decreased significantly compared to the control group, and Prevotella was basically exhausted. Download figure Open in new tab Download figure Open in new tab Figure 3. Effects of antibiotics and probiotics on intestinal microbiota A: Phylum level of intestinal microbiota; B: Genus level of intestinal microbiota; C: α diversity; D: β diversity; E: Analysis of marker species; *** P <0.001 Compared to the control group, the richness and evenness of bacteria in the antibiotic group decreased significantly based on α-diversity indices ( P <0.001). In contrast, the changes in the probiotic group were not statistically significant ( Figure 3C ). The PCoA plot shows a discriminated β-diversity between the control and antibiotic groups ( Figure 3D ). LEfSe results show that Sphaerochaeta, Shigella, Anaeroplasma, and Sutterella are the signature species in the antibiotic group and were significantly enriched. In contrast, Prevotella , Oscillospira , Allobaculum , and Ruminococcus are the predominant species in the probiotic group and were significantly increased ( Figure 3E , P <0.05, LDA ≥ 4.0). 3.4 Correlation between the intestinal microbiota and the pharmacokinetic parameters of NBP and CYP3A1 expression The relative abundance of 20 genera showed a correlation between the pharmacokinetic parameters and the CYP3A1 expression ( Figure 4 ). The relative abundance of Anaeroplasma , Paenibacillus , and Sphaerochaeta is positively correlated with AUC 0-8 or C max but negatively correlated with CYP3A1 expression. Bacteroides , Prevotella , Clostridium , and Ruminococcus have a positive correlation with CL and CYP3A1 mRNA expressions. Among the four genera of bacteria supplemented with probiotics, only Bifidobacterium is both negatively correlated with AUC 0-8 but positively correlated with CYP3A1 mRNA expression in the small intestine. The CYP3A1 protein expression in the liver was negatively regulated by Oscillospira . Download figure Open in new tab Figure 4. Correlation of the horizontal abundance of the intestinal microbiota genus with the pharmacokinetic parameters of n-butylphthalide and the expression of CYP3A1 * P <0.05; ** P <0.01 3.5 Effects of intestinal microbiota alteration on intestinal metabolic pathways KEGG analysis shows the effects of antibiotics and probiotics on intestinal metabolic pathways ( Figure 5 ). Changes in the intestinal microbiota had the greatest influence on the KEGG primary pathway (L1) of Metabolism ( Figure 5A ). Analysis of the KEGG secondary pathway (L2) in the Metabolic pathway shows that “ Carbohydrate metabolism,” “ Glycan biosynthesis and metabolism,” “ Lipid metabolism,” “ Metabolism of cofactors and vitamins,” and “ Xenobiotics biodegradation and metabolism” pathways were inhibited in the antibiotic group compared to the control group ( Figure 5B , P <0.001). Analysis of the KEGG level 3 pathway (L3) shows no significant differences in enrichment pathways in the probiotic group. In contrast, there were 37 significant differences in the antibiotic group ( Figure 5C , P <0.05, LDA ≥ 3). Download figure Open in new tab Download figure Open in new tab Download figure Open in new tab Figure 5. Effects of the intestinal microbiota on metabolic pathways A: Effect of the intestinal microbiota on the KEGG L1 pathway; B: Abundance comparison of KEGG L2 metabolic pathways; C: Comparison of significantly enriched KEGG L3 pathways; D: Correlation between horizontal abundance and the KEGG L3 pathway; * P <0.05; ** P <0.01; *** P <0.001 Twenty relatively abundant genera were selected to analyze the correlation between the intestinal microbiota and the metabolic pathways of NBP ( Figure 5D ). The Sphaerochaeta , Shigella , Anaeroplasma, and Sutterella antibiotic group marker genera were negatively correlated with “ Secondary bile acid biosynthesis,” “ Drug metabolism-other enzyme,” and “ Fatty acid biosynthesis.” In contrast, these genera were positively associated with “ Fatty acid metabolism ” and “ Biosynthesis of unsaturated fatty acids .” These correlations were reversed for Lactobacillus, Prevotella , and Oscillospira . The “ Metabolism of xenobiotics by cytochrome P450” was positively correlated with the relative abundance of Pediococcus . 4 Discussion In recent years, as pharmacomicrobiomics has received increasing attention [ 13 ], the impact of intestinal flora changes on drug pharmacokinetics and pharmacodynamics has become a research hotspot [ 14 – 16 ]. CYP3A4 is the main metabolic enzyme of NBP in humans, and CYP3A1 in SD rats is the homologous enzyme of human CYP3A4 [ 17 ]. Therefore, this study explored the effects of intestinal flora environments on NBP pharmacokinetics and CYP3A1 expressions by administering antibiotics and probiotics to SD rats. 4.1 Effects of intestinal microbiota on the pharmacokinetics of drugs Probiotics can have various beneficial effects on human health [ 18 ]. However, when combined with drugs, probiotics can change drug pharmacokinetics through several possible mechanisms. First, the intestinal flora secretes a variety of phase I and phase II metabolic enzymes, which can directly participate in the metabolism of oral drugs and affect the efficacy and toxicity of drugs [ 19 – 21 ]. Second, the alteration of the intestinal microbiota influences the expression of liver drug metabolism enzymes [ 22 , 23 ]. Secondary bile acids and short-chain fatty acids (SCFAs), the metabolites of intestinal flora, can enter the liver through the portal vein and alter mRNA expression of multiple drug enzymes [ 24 – 27 ].Furthermore, the intestinal microbiota can also affect intestinal drug transporters [ 28 , 29 ], maintain intestinal structure and mucosal integrity, and alter drug absorption [ 30 ]. 4.2 Effects of antibiotic treatment on intestinal microbiota and NBP metabolism Vancomycin was chosen as the interventional antibiotic in this study because it is not absorbed orally. It has a high intestinal concentration and a marked inhibitory effect on certain intestinal bacteria, leading to substantial changes in the intestinal microbiota [ 31 ]. In addition, vancomycin does not induce host metabolic enzymes. It does not have drug-drug interactions mediated by metabolic enzymes and is suitable for studying the pharmacokinetics of NBP as an antibiotic for intestinal flora intervention. Lithocholic acid, one of the pregnane X receptors (PXR) ligands, is produced mainly by the normal intestinal dominant phyla Bacteroides and Firmicutes , which can up-regulate CYP3A1 mRNA expressions and increase the metabolism of CYP3A1 substrates [ 32 – 34 ]. Another PXR ligand, indole-3-propionic acid (IPA) [ 35 ], is the active product of tryptophan. Clostridium mediates the conversion of tryptophan to IPA and up-regulates the CYP3A1 mRNA expression. This study demonstrates that antibiotic gavage decreases the relative abundance of Bacteroidetes and Firmicutes , among them Clostridium, leading to a down-regulation of PXR-regulated CYP3A1 expression. Previous studies have demonstrated that depletion of Prevotellaceae and enrichment with Shigella reduce intestinal butyrate production and downregulate the expression of host CYP450 enzymes [ 36 ]. The present study showed that in the antibiotic group, the relative abundance of Lactobacillus and Prevotella decreased, while the relative abundance of Sphaerochaeta and Shigella increased. The expression of CYP3A1 mRNA in the small intestine was significantly down-regulated, and CYP3A1 mRNA and protein expressions in the liver were significantly decreased. The results are consistent with previous research. The results of PICRUSt2 showed that antibiotic-induced changes in the microbiota could inhibit metabolism-related pathways, thus increasing the bioavailability of NBP. The relative abundance of probiotics Lactobacillus, Prevotella, Bacteroides, and Oscillospira decreased in the antibiotic group, and metabolic pathways such as “ secondary bile acid biosynthesis ,” “ fatty acid biosynthesis ,” and “ drug metabolism ” were significantly down-regulated. The relative abundance of Pediococcus was positively correlated with intestinal “ metabolism of xenobiotics by CYP450 ”. This suggests that intestinal microbiota imbalance, metabolic disturbance, and antibiotic-induced decreased production of metabolites such as secondary bile acids and SCFAs are the main reasons for the increased oral bioavailability of NBP. 4.3 Effects of probiotic gavage on intestinal microbiota and NBP metabolism The probiotics used in this study were a mixture of four live bacteria. Among them, Bifidobacterium , Lactobacillus, and Enterococ are the normal flora of the human intestinal tract, which can grow, reproduce, and colonize the intestinal tract after oral administration. They can inhibit some pathogenic bacteria in the intestinal tract, maintain normal intestinal peristalsis, and regulate the balance of intestinal flora. Bacillus can create an anaerobic environment in addition to the normal flora in the human intestinal tract and promote the growth and reproduction of anaerobic bacteria such as Bifidobacteria . Previous studies have shown that the effects of bifidobacterial tetrads on the host are mainly mediated by metabolites rather than by modulation of bacterial diversity [ 37 ]. This study showed that probiotic treatment did not cause significant changes in intestinal microbiota diversity, consistent with previous studies. After supplementation with probiotics, the relative abundance of Bifidobacterium , Lactobacillus, and Oscillospira increased, which can promote the production of SCFAs [ 38 ]. However, there were no significant differences in the expressions of CYP3A1 and KEGG metabolic pathways compared to the control group, which may be related to individual differences [ 39 ]. In previous studies [ 40 , 41 ], the effect of changes in the intestinal microbiota on drug pharmacokinetics was not consistent because differences in the type, dose, and treatment cycle of probiotics may have different effects on drug pharmacokinetics. 5. Conclusions This study demonstrates that antibiotic-induced alterations in the intestinal microbiota can increase the oral bioavailability of NBP. However, CYP3A1 expression and metabolic pathways did not show significant differences in the probiotic group compared to the control group. Therefore, further studies designed to observe the effect of single probiotic supplementation on drug metabolism are warranted. Furthermore, the relationship between pharmacokinetics and pharmacodynamics is complex. Additional validation is needed to determine whether changes in NBP pharmacokinetics caused by antibiotic-mediated alterations in the intestinal microbiota affect its clinical efficacy. Funding This project was supported by the Natural Science Foundation of Hebei Province (number: H202106384). Conflicts of interest statement Authors declare no conflicts of interest. Acknowledgments The authors thank Dong Weichong, Li Wenli and other colleagues for participation in this study. References 1. ↵ Singh V , Sadler R , Heindl S , et al. The gut microbiome primes a cerebroprotective immune response after stroke . J Cereb Blood Flow Metab . 2018 Aug; 38 ( 8 ): 1293 – 1298 . OpenUrl CrossRef PubMed 2. ↵ Benakis C , Poon C , Lane D , et al. Distinct commensal bacterial signature in the gut is associated with acute and long-term protection from ischemic stroke . Stroke . 2020 Jun; 51 ( 6 ): 1844 – 1854 . OpenUrl CrossRef PubMed 3. ↵ 18. Benakis C , Brea D , Caballero S , et al. 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